library(DiagrammeR)
gp_nodes = create_nodes(    
  nodes = c('GP w/ Matern', 'GP w/ Matern & ν fixed', 'GP w/ Matern & ν = ∞','GP w/ SqExp', 
            'RKHS', 'AR(1)', 'OU'),
  type = 'a',
  value = 1,
  label = TRUE,
  
  style = 'filled',
  color = 'papayawhip',
  shape = 'circle',
  fixedsize=T,
  distortion='',
  fillcolor='',
  fixedsize='',
  fontcolor='',
  fontname='',
  fontsize=3,
  height='',
  penwidth='',
  peripheries='',
  shape='',
  sides='',
  # tooltip='FU',
  width='',
  x='',
  y='')

edges = create_edges(
  from = '',
  to = '',
  rel = '',
  distortion='',
  fillcolor='',
  fixedsize='',
  fontcolor='',
  fontname='',
  fontsize='',
  height='',
  penwidth='',
  peripheries='',
  shape='',
  sides='',
  style='',
  tooltip='',
  width='',
  x='',
  y=''
)
render_graph(create_graph(gp_nodes))

Connections to Gaussian processes.

Labels

GP = Gaussian process SqExp = squared exponential covariance structure Matern = Matern covariances structure OU = Ornstein-Uhlenbeck \(\mathcal{l}\) = horizontal length-scale \(\mathcal{\nu}\) = controls differentiability

References

Rasmussen & Williams (2006). Gaussian Processes for Machine Learning. Murphy (2012). Machine Learning: A probabilistic perspective.